Parents with disabilities, mandated reporting, and nursing: A scoping review.

BACKGROUND: Many nurses report feeling underprepared and insecure about the mandated reporting of concern for child maltreatment, which is further compounded by parental disability. PURPOSE: This scoping review examined the body of literature on nurse mandated reporting responsibility within the context of parental disability. METHODS: A scoping review was conducted as informed by the Arksey and O'Malley Framework and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. DISCUSSION: Initial database search identified 2,669 records, which then underwent title and abstract screening (n = 2458), assessment for full text review (n = 53), and full review (n = 28). Analysis of 106 quantitative and 22 qualitative variables revealed sparse evidence and guidance to help nurses navigate mandated reporting of child abuse in families with parental disability. CONCLUSION: Findings identified opportunities to inform research, policy, and practice to better meet the needs of nurse mandated reporters and families with parental disability.

Shrinking the footprint of the criminal legal system through policies informed by psychology and neuroscience.

The footprint of the legal system in the United States is expansive. Applying psychological and neuroscience research to understand or predict individual criminal behavior is problematic. Nonetheless, psychology and neuroscience can contribute substantially to the betterment of the criminal legal system and the outcomes it produces. We argue that scientific findings should be applied to the legal system through systemwide policy changes. Specifically, we discuss how science can shape policies around pollution in prisons, the use of solitary confinement, and the law's conceptualization of insanity. Policies informed by psychology and neuroscience have the potential to affect meaningful-and much-needed-legal change.

Hippocampal neuronal activity is aligned with action plans.

Neurons in the hippocampus are correlated with different variables, including space, time, sensory cues, rewards, and actions, where the extent of tuning depends on ongoing task demands. However, it remains uncertain whether such diverse tuning corresponds to distinct functions within the hippocampal network or if a more generic computation can account for these observations. To disentangle the contribution of externally driven cues versus internal computation, we developed a task in mice where space, auditory tones, rewards, and context were juxtaposed with changing relevance. High-density electrophysiological recordings revealed that neurons were tuned to each of these modalities. By comparing movement paths and action sequences, we observed that external variables had limited direct influence on hippocampal firing. Instead, spiking was influenced by online action plans modulated by goal uncertainty. Our results suggest that internally generated cell assembly sequences are selected and updated by action plans toward deliberate goals. The apparent tuning of hippocampal neuronal spiking to different sensory modalities might emerge due to alignment to the afforded action progression within a task rather than representation of external cues.

Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models.

Neyman-Scott processes (NSPs) are point process models that generate clusters of points in time or space. They are natural models for a wide range of phenomena, ranging from neural spike trains to document streams. The clustering property is achieved via a doubly stochastic formulation: first, a set of latent events is drawn from a Poisson process; then, each latent event generates a set of observed data points according to another Poisson process. This construction is similar to Bayesian nonparametric mixture models like the Dirichlet process mixture model (DPMM) in that the number of latent events (i.e. clusters) is a random variable, but the point process formulation makes the NSP especially well suited to modeling spatiotemporal data. While many specialized algorithms have been developed for DPMMs, comparatively fewer works have focused on inference in NSPs. Here, we present novel connections between NSPs and DPMMs, with the key link being a third class of Bayesian mixture models called mixture of finite mixture models (MFMMs). Leveraging this connection, we adapt the standard collapsed Gibbs sampling algorithm for DPMMs to enable scalable Bayesian inference on NSP models. We demonstrate the potential of Neyman-Scott processes on a variety of applications including sequence detection in neural spike trains and event detection in document streams.

Unsupervised discovery of family specific vocal usage in the Mongolian gerbil.

In nature, animal vocalizations can provide crucial information about identity, including kinship and hierarchy. However, lab-based vocal behavior is typically studied during brief interactions between animals with no prior social relationship, and under environmental conditions with limited ethological relevance. Here, we address this gap by establishing long-term acoustic recordings from Mongolian gerbil families, a core social group that uses an array of sonic and ultrasonic vocalizations. Three separate gerbil families were transferred to an enlarged environment and continuous 20-day audio recordings were obtained. Using a variational autoencoder (VAE) to quantify 583,237 vocalizations, we show that gerbils exhibit a more elaborate vocal repertoire than has been previously reported and that vocal repertoire usage differs significantly by family. By performing gaussian mixture model clustering on the VAE latent space, we show that families preferentially use characteristic sets of vocal clusters and that these usage preferences remain stable over weeks. Furthermore, gerbils displayed family-specific transitions between vocal clusters. Since gerbils live naturally as extended families in complex underground burrows that are adjacent to other families, these results suggest the presence of a vocal dialect which could be exploited by animals to represent kinship. These findings position the Mongolian gerbil as a compelling animal model to study the neural basis of vocal communication and demonstrates the potential for using unsupervised machine learning with uninterrupted acoustic recordings to gain insights into naturalistic animal behavior.

Vocal Call Locator Benchmark (VCL) for localizing rodent vocalizations from multi-channel audio.

Understanding the behavioral and neural dynamics of social interactions is a goal of contemporary neuroscience. Many machine learning methods have emerged in recent years to make sense of complex video and neurophysiological data that result from these experiments. Less focus has been placed on understanding how animals process acoustic information, including social vocalizations. A critical step to bridge this gap is determining the senders and receivers of acoustic information in social interactions. While sound source localization (SSL) is a classic problem in signal processing, existing approaches are limited in their ability to localize animal-generated sounds in standard laboratory environments. Advances in deep learning methods for SSL are likely to help address these limitations, however there are currently no publicly available models, datasets, or benchmarks to systematically evaluate SSL algorithms in the domain of bioacoustics. Here, we present the VCL Benchmark: the first large-scale dataset for benchmarking SSL algorithms in rodents. We acquired synchronized video and multi-channel audio recordings of 767,295 sounds with annotated ground truth sources across 9 conditions. The dataset provides benchmarks which evaluate SSL performance on real data, simulated acoustic data, and a mixture of real and simulated data. We intend for this benchmark to facilitate knowledge transfer between the neuroscience and acoustic machine learning communities, which have had limited overlap.

Perpetual step-like restructuring of hippocampal circuit dynamics.

Representation of the environment by hippocampal populations is known to drift even within a familiar environment, which could reflect gradual changes in single-cell activity or result from averaging across discrete switches of single neurons. Disambiguating these possibilities is crucial, as they each imply distinct mechanisms. Leveraging change point detection and model comparison, we find that CA1 population vectors decorrelate gradually within a session. In contrast, individual neurons exhibit predominantly step-like emergence and disappearance of place fields or sustained changes in within-field firing. The changes are not restricted to particular parts of the maze or trials and do not require apparent behavioral changes. The same place fields emerge, disappear, and reappear across days, suggesting that the hippocampus reuses pre-existing assemblies, rather than forming new fields de novo. Our results suggest an internally driven perpetual step-like reorganization of the neuronal assemblies.

Perpetual step-like restructuring of hippocampal circuit dynamics.

Representation of the environment by hippocampal populations is known to drift even within a familiar environment, which could reflect gradual changes in single cell activity or result from averaging across discrete switches of single neurons. Disambiguating these possibilities is crucial, as they each imply distinct mechanisms. Leveraging change point detection and model comparison, we found that CA1 population vectors decorrelated gradually within a session. In contrast, individual neurons exhibited predominantly step-like emergence and disappearance of place fields or sustained change in within-field firing. The changes were not restricted to particular parts of the maze or trials and did not require apparent behavioral changes. The same place fields emerged, disappeared, and reappeared across days, suggesting that the hippocampus reuses pre-existing assemblies, rather than forming new fields de novo. Our results suggest an internally-driven perpetual step-like reorganization of the neuronal assemblies.

Plant-microbe interactions through a lens: tales from the mycorrhizosphere.

BACKGROUND: The soil microbiome plays a pivotal role in maintaining ecological balance, supporting food production, preserving water quality and safeguarding human health. Understanding the intricate dynamics within the soil microbiome necessitates unravelling complex bacterial-fungal interactions (BFIs). BFIs occur in diverse habitats, such as the phyllosphere, rhizosphere and bulk soil, where they exert substantial influence on plant-microbe associations, nutrient cycling and overall ecosystem functions. In various symbiotic associations, fungi form mycorrhizal connections with plant roots, enhancing nutrient uptake through the root and mycorrhizal pathways. Concurrently, specific soil bacteria, including mycorrhiza helper bacteria, play a pivotal role in nutrient acquisition and promoting plant growth. Chemical communication and biofilm formation further shape plant-microbial interactions, affecting plant growth, disease resistance and nutrient acquisition processes. SCOPE: Promoting synergistic interactions between mycorrhizal fungi and soil microbes holds immense potential for advancing ecological knowledge and conservation. However, despite the significant progress, gaps remain in our understanding of the evolutionary significance, perception, functional traits and ecological relevance of BFIs. Here we review recent findings obtained with respect to complex microbial communities - particularly in the mycorrhizosphere - and include the latest advances in the field, outlining their profound impacts on our understanding of ecosystem dynamics and plant physiology and function. CONCLUSIONS: Deepening our understanding of plant BFIs can help assess their capabilities with regard to ecological and agricultural safe-guarding, in particular buffering soil stresses, and ensuring sustainable land management practices. Preserving and enhancing soil biodiversity emerge as critical imperatives in sustaining life on Earth amidst pressures of anthropogenic climate change. A holistic approach integrates scientific knowledge on bacteria and fungi, which includes their potential to foster resilient soil ecosystems for present and future generations.

Mucoromycotina 'fine root endophytes': a new molecular model for plant-fungal mutualisms?

The most studied plant-fungal symbioses to date are the interactions between plants and arbuscular mycorrhizal (AM) fungi of the Glomeromycotina clade. Advancements in phylogenetics and microbial community profiling have distinguished a group of symbiosis-forming fungi that resemble AM fungi as belonging instead to the Mucoromycotina. These enigmatic fungi are now known as Mucoromycotina 'fine root endophytes' and could provide a means to understand the origins of plant-fungal symbioses. Most of our knowledge of the mechanisms of fungal symbiosis comes from investigations using AM fungi. Here, we argue that inclusion of Mucoromycotina fine root endophytes in future studies will expand our understanding of the mechanisms, evolution, and ecology of plant-fungal symbioses.

Large-scale manufacturing of base-edited chimeric antigen receptor T cells.

Base editing is a revolutionary gene-editing technique enabling the introduction of point mutations into the genome without generating detrimental DNA double-stranded breaks. Base-editing enzymes are commonly delivered in the form of modified linear messenger RNA (mRNA) that is costly to produce. Here, we address this problem by developing a simple protocol for manufacturing base-edited cells using circular RNA (circRNA), which is less expensive to synthesize. Compared with linear mRNA, higher editing efficiencies were achieved with circRNA, enabling an 8-fold reduction in the amount of RNA required. We used this protocol to manufacture a clinical dose (1 × 108 cells) of base-edited chimeric antigen receptor (CAR) T cells lacking expression of the inhibitory receptor, PD-1. Editing efficiencies of up to 86% were obtained using 0.25 µg circRNA/1 × 106 cells. Increased editing efficiencies with circRNA were attributed to more efficient translation. These results suggest that circRNA, which is less expensive to produce than linear mRNA, is a viable option for reducing the cost of manufacturing base-edited cells at scale.

Remapping in a recurrent neural network model of navigation and context inference.

Neurons in navigational brain regions provide information about position, orientation, and speed relative to environmental landmarks. These cells also change their firing patterns ('remap') in response to changing contextual factors such as environmental cues, task conditions, and behavioral states, which influence neural activity throughout the brain. How can navigational circuits preserve their local computations while responding to global context changes? To investigate this question, we trained recurrent neural network models to track position in simple environments while at the same time reporting transiently-cued context changes. We show that these combined task constraints (navigation and context inference) produce activity patterns that are qualitatively similar to population-wide remapping in the entorhinal cortex, a navigational brain region. Furthermore, the models identify a solution that generalizes to more complex navigation and inference tasks. We thus provide a simple, general, and experimentally-grounded model of remapping as one neural circuit performing both navigation and context inference.

Genome-wide association study of chronic sputum production implicates loci involved in mucus production and infection.

BACKGROUND: Chronic sputum production impacts on quality of life and is a feature of many respiratory diseases. Identification of the genetic variants associated with chronic sputum production in a disease agnostic sample could improve understanding of its causes and identify new molecular targets for treatment. METHODS: We conducted a genome-wide association study (GWAS) of chronic sputum production in UK Biobank. Signals meeting genome-wide significance (p<5×10-8) were investigated in additional independent studies, were fine-mapped and putative causal genes identified by gene expression analysis. GWASs of respiratory traits were interrogated to identify whether the signals were driven by existing respiratory disease among the cases and variants were further investigated for wider pleiotropic effects using phenome-wide association studies (PheWASs). RESULTS: From a GWAS of 9714 cases and 48 471 controls, we identified six novel genome-wide significant signals for chronic sputum production including signals in the human leukocyte antigen (HLA) locus, chromosome 11 mucin locus (containing MUC2, MUC5AC and MUC5B) and FUT2 locus. The four common variant associations were supported by independent studies with a combined sample size of up to 2203 cases and 17 627 controls. The mucin locus signal had previously been reported for association with moderate-to-severe asthma. The HLA signal was fine-mapped to an amino acid change of threonine to arginine (frequency 36.8%) in HLA-DRB1 (HLA-DRB1*03:147). The signal near FUT2 was associated with expression of several genes including FUT2, for which the direction of effect was tissue dependent. Our PheWAS identified a wide range of associations including blood cell traits, liver biomarkers, infections, gastrointestinal and thyroid-associated diseases, and respiratory disease. CONCLUSIONS: Novel signals at the FUT2 and mucin loci suggest that mucin fucosylation may be a driver of chronic sputum production even in the absence of diagnosed respiratory disease and provide genetic support for this pathway as a target for therapeutic intervention.

Does a change in end-tidal carbon dioxide level predict high altitude mountain sickness?

Background: It is postulated that lack of hypoxic ventilatory response is a predictor for AMS. End-tidal carbon dioxide (ETCO2) is an accurate, noninvasive surrogate measure of ventilation. Objectives: We sought to determine if changes in baseline ETCO2 predicts the development of AMS. Methods: This prospective cohort study took place in three separate high-altitude hiking treks. Subjects included a convenience sample of hikers. Predictor variable was change in ETCO2 levels and outcome variable was AMS. Measurements of ETCO2 levels were obtained at the base and repeated daily at various elevations and the summit of each hike. Concurrently, hikers were scored for AMS by a trained investigator. We utilized correlation coefficients and developed a linear regression model for analysis. Results: 21 subjects in 3 separate hikes participated: 10 ascended to 19,341 ft over 7 days, 6 ascended to 8900 ft in 1 day, and 4 ascended to 11,006 ft in 1 day. Mean age was 40 years, 67% were males, mean daily elevation gain was 2150 ft, and 5 hikers developed AMS. The correlation coefficients for ETCO2 and development of AMS were -0.46 (95%CI -0.33 to -0.57), and -0.77 (95%CI -0.71 to -0.83) for ETCO2 and altitude. ETCO2 predicted the development of symptoms better than the elevation with AUCs of 0.90 (95%CI 0.81-0.99) versus 0.64 (95%CI 0.45-0.83). An ETCO2 measurement of ≤22 mmHg was 100% sensitive and 60% specific for predicting AMS. Conclusions: ETCO2 was strongly correlated with altitude and moderately correlated with AMS and it was a better predictor than altitude.

REFINE-Lung implements a novel multi-arm randomised trial design to address possible immunotherapy overtreatment.

Increasing evidence suggests that some immunotherapy dosing regimens for patients with advanced cancer could result in overtreatment. Given the high costs of these agents, and important implications for quality of life and toxicity, new approaches are needed to identify and reduce unnecessary treatment. Conventional two-arm non-inferiority designs are inefficient in this context because they require large numbers of patients to explore a single alternative to the standard of care. Here, we discuss the potential problem of overtreatment with anti-PD-1 directed agents in general and introduce REFINE-Lung (NCT05085028), a UK multicentre phase 3 study of reduced frequency pembrolizumab in advanced non-small-cell lung cancer. REFINE-Lung uses a novel multi-arm multi-stage response over continuous interventions (MAMS-ROCI) design to determine the optimal dose frequency of pembrolizumab. Along with a similarly designed basket study of patients with renal cancer and melanoma, REFINE-Lung and the MAMS-ROCI design could contribute to practice-changing advances in patient care and form a template for future immunotherapy optimisation studies across cancer types and indications. This new trial design is applicable to many new or existing agents for which optimisation of dose, frequency, or duration of therapy is desirable.

Equity Lens Protocol: Reflecting on Harms From the COVID-19 Response and Mitigation Strategies Implemented in a Local Public Health System.

This report describes an Equity Lens Protocol and its use to guide partners' systematic reflection on harms and mitigation strategies of the COVID-19 response in a local public health system. This process evaluation tool is based on the Guidance document for assuring an equitable response to COVID-19 prepared by the Pan American Health Organization. We used a participatory approach to engage public health partners in systematically reflecting on harms, mitigation strategies, and lessons learned and implications for practice. Outputs from using this tool included identified: (a) specific harms (e.g., loss of income and challenges to learning) related to particular COVID-19 response measures (e.g., home confinement and school closure) and (b) mitigation strategies implemented to reduce harms. In response to the protocol's guiding questions, partners also identified lessons learned and practice recommendations for strengthening equity work in public health responses (e.g., an equitable response requires an investment in people, structures, and relationships before a crisis). This report-and accompanying protocol-illustrates use of a practical method for systematic reflection on public health responses through an equity lens.

Untangling the Complexities of Processing and Analysis for Untargeted LC-MS Data Using Open-Source Tools.

Untargeted metabolomics is a powerful tool for measuring and understanding complex biological chemistries. However, employment, bioinformatics and downstream analysis of mass spectrometry (MS) data can be daunting for inexperienced users. Numerous open-source and free-to-use data processing and analysis tools exist for various untargeted MS approaches, including liquid chromatography (LC), but choosing the 'correct' pipeline isn't straight-forward. This tutorial, in conjunction with a user-friendly online guide presents a workflow for connecting these tools to process, analyse and annotate various untargeted MS datasets. The workflow is intended to guide exploratory analysis in order to inform decision-making regarding costly and time-consuming downstream targeted MS approaches. We provide practical advice concerning experimental design, organisation of data and downstream analysis, and offer details on sharing and storing valuable MS data for posterity. The workflow is editable and modular, allowing flexibility for updated/changing methodologies and increased clarity and detail as user participation becomes more common. Hence, the authors welcome contributions and improvements to the workflow via the online repository. We believe that this workflow will streamline and condense complex mass-spectrometry approaches into easier, more manageable, analyses thereby generating opportunities for researchers previously discouraged by inaccessible and overly complicated software.

Spontaneous variations in arousal modulate subsequent visual processing and local field potential dynamics in the ferret during quiet wakefulness.

Behavioral states affect neuronal responses throughout the cortex and influence visual processing. Quiet wakefulness (QW) is a behavioral state during which subjects are quiescent but awake and connected to the environment. Here, we examined the effects of pre-stimulus arousal variability on post-stimulus neural activity in the primary visual cortex and posterior parietal cortex in awake ferrets, using pupil diameter as an indicator of arousal. We observed that the power of stimuli-induced alpha (8-12 Hz) decreases when the arousal level increases. The peak of alpha power shifts depending on arousal. High arousal increases inter- and intra-areal coherence. Using a simplified model of laminar circuits, we show that this connectivity pattern is compatible with feedback signals targeting infragranular layers in area posterior parietal cortex and supragranular layers in V1. During high arousal, neurons in V1 displayed higher firing rates at their preferred orientations. Broad-spiking cells in V1 are entrained to high-frequency oscillations (>80 Hz), whereas narrow-spiking neurons are phase-locked to low- (12-18 Hz) and high-frequency (>80 Hz) rhythms. These results indicate that the variability and sensitivity of post-stimulus cortical responses and coherence depend on the pre-stimulus behavioral state and account for the neuronal response variability observed during repeated stimulation.

Remapping in a recurrent neural network model of navigation and context inference.

Neurons in navigational brain regions provide information about position, orientation, and speed relative to environmental landmarks. These cells also change their firing patterns ("remap") in response to changing contextual factors such as environmental cues, task conditions, and behavioral state, which influence neural activity throughout the brain. How can navigational circuits preserve their local computations while responding to global context changes? To investigate this question, we trained recurrent neural network models to track position in simple environments while at the same time reporting transiently-cued context changes. We show that these combined task constraints (navigation and context inference) produce activity patterns that are qualitatively similar to population-wide remapping in the entorhinal cortex, a navigational brain region. Furthermore, the models identify a solution that generalizes to more complex navigation and inference tasks. We thus provide a simple, general, and experimentally-grounded model of remapping as one neural circuit performing both navigation and context inference.